| Title: | Machine learning reveals genetic modifiers of the immune microenvironment of cancer |
| Journal: | iScience |
| Published: | 1 Sep 2023 |
| DOI: | https://doi.org/10.1016/j.isci.2023.107576 |
Publication 7680
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Abstract
Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.11 Authors
- Bridget Riley-Gillis
- Shirng-Wern Tsaih
- Emily King
- Sabrina Wollenhaupt
- Jonas Reeb
- Amy R. Peck
- Kelsey Wackman
- Angela Lemke
- Hallgeir Rui
- Zoltan Dezso
- Michael J. Flister
1 Application
| Application ID | Title |
|---|---|
| 26041 | Large-Scale Sequencing in the UK Biobank to Facilitate Gene Discovery, Genome Sciences, and Precision Medicine |
Enabling scientific discoveries that improve human health